Efficient Market Hypothesis The Empirics

4 basic traits of efficiency An efficient market exhibits certain behavioral traits that becomes necessary conditions. We can examine the real capital market to see if it conforms with these traits. If it doesn’t, we can conclude that the market is inefficient. (But not vice versa) 1.Act to new information quickly and accurately 2.Price movement is unpredictable (memory-less) 3.No trading strategy consistently beat the market 4.Investment professionals not that professional What if?DefinitionsImplicationsPriceEmpirics

Empirical Strategies Look at the historical data. See if they conform with the 4 traits This requires: –Identifying the hypotheses to be tested –Collect the needed historical data –Structure the historical data for statistical hypothesis test –Perform tests –Identify results/conclusions and potential problems What if?DefinitionsImplicationsPriceEmpirics

1 st trait: reaction to news What if?DefinitionsImplicationsPriceEmpirics 0+t-t The announcement of a positive news Days relative to announcement day Stock price ($) Early Reaction Delayed Reaction

1 st trait: reaction to news Event study “One type of tests of the semi-strong form efficient market hypothesis to see if prices reflect all publicly available information.” Event studies examine prices and returns over time (particularly around the arrival of new information.) Test for evidence of [1] under-reaction, [2] over-reaction, [3] early- reaction, [4] delayed-reaction around the event. If market is “semi-strong-form efficient”, the effects of an event will be reflected immediately in security prices. Thus a measure of the event’s economic impact can be constructed using security prices observed over a relatively short time period. Some examples of events include –mergers and acquisitions, –earnings announcements, –issues of new debt or equity, –stock splits, –announcements of macroeconomic variables such as the trade figures, –Health issue of the key person of a company. What if?DefinitionsImplicationsPriceEmpirics

1 st trait: reaction to news The mechanics of an event study: Sample - stockS. [1] Identify the event of interest. (e.g., stock splits) [2] Define the event period? (e.g., -10 to +10 days relative to the event date) [3] Select the sample (e.g., firms which have in common the incidence of the event of interest) [4] Measure the impact by means of abnormal return [5] Estimate the parameters needed to calculate expected returns. [6] Calculate cumulative abnormal returns What if?DefinitionsImplicationsPriceEmpirics

1 st trait: reaction to news The event period should start before you think the event has an effect on the stock price. As an example, for merger announcements, a typical choice is from 25 trading days before the announcement day to 25 trading days after the announcement day The estimation period should be a period right before the event period. For merger announcements, a typical choice is 100 trading days before the start of the event period What if?DefinitionsImplicationsPriceEmpirics Event PeriodEstimation Period -125

1 st trait: reaction to news Abnormal return = Actual realized return – Expected return E.g., E(R j |R M,t ) = a 0 + a j R M,t (Return on security j conditional on the return on market) ε j,t = R j,t – E((R j |R M,t ) Cumulative abnormal return: CAR j,t = ∑ -T t ε j,t (Aggregate abnormal returns from –T to t) Average cumulative abnormal return over a sample of securities: Average CAR t = (∑ j CAR j,t )/J (where J = no. of securities in the sample) Plot the graph, examine the pattern. Of course, perform hypothesis testing as well. What if?DefinitionsImplicationsPriceEmpirics

1 st trait: reaction to news What if?DefinitionsImplicationsPriceEmpirics Efficient market response to “bad news” Source: Szewczyk, Tsetsekos and Santout (1997)Szewczyk, Tsetsekos and Santout (1997)

1 st trait: reaction to news What if?DefinitionsImplicationsPriceEmpirics How stock splits affect value? Source: Fama, Fisher, Jensen & Roll (1969)Fama, Fisher, Jensen & Roll (1969)

1 st trait: reaction to news What if?DefinitionsImplicationsPriceEmpirics Announcement Date

1 st trait: reaction to news What if?DefinitionsImplicationsPriceEmpirics Announcement Date for quarterly earnings reports Days relative to Announcement Date Average Cumulative abnormal return Source: Rendleman, Jones and Latane (1982)Rendleman, Jones and Latane (1982)

1 st trait: reaction to news Event study methodology has been applied to a large number of events including: –Dividend increases and decreases –Earnings announcements –Mergers –Capital Spending –New Issues of Stock The studies generally support the view that the market is semi-strong form efficient. In fact, the studies suggest that markets may even have some foresight into the future—in other words, news tends to leak out in advance of public announcements. (What does that imply?) What if?DefinitionsImplicationsPriceEmpirics

2 nd trait: Random price movements Studies of serial correlation Studies of seasonality –Day of the week effectDay of the week effect –January effectJanuary effect Test of weak-form efficiency What if?DefinitionsImplicationsPriceEmpirics

2 nd trait: Random price movements Studies of serial correlation NULL HYPOTHESIS: H 0 : Cov(ΔP t, ΔP t-i ) is significantly different from zero or not, for i ≠ 0 Or alternatively, the following null hypothesis: H 0 : Cov(Δr t, Δr t-i ) is significantly different from zero or not, for i ≠ 0 Plot the following types of graph. Note: Statistically significant ≠ Economically significant –If you are aware of the correlation, and attempt to trade on the basis of it, brokerage commissions, taxes, and other transaction costs may make your expected profits negative. What if?DefinitionsImplicationsPriceEmpirics

2 nd trait: Random price movements What if?DefinitionsImplicationsPriceEmpirics Return on day t (in %) Return on day t+1 (in %)

2 nd trait: Random price movements What if?DefinitionsImplicationsPriceEmpirics Return on week t+1 (in %) Return on week t (in %) FTSE 100 FTSE 100 (correlation = -0.08)Nikkei 500 Nikkei 500 (correlation = -0.06) DAX 30 DAX 30 (Correlation = -0.03)S & P Composite S & P Composite (correlation = -0.07)

2 nd trait: Random price movements Studies of seasonality –Day of the week effect –French (1980) and Gibbons & Hess (1981)French (1980)Gibbons & Hess (1981) –Using S&P 500 index to proxy returns of stocks for each of the 5 trading days of the week. “Although the average return for the other four days of the week was positive, the average for Monday was significantly negative during each of five five-year sub-periods.” French (1980) “Perhaps the most obvious explanation for this result is that the information released over the weekend tends to be unfavorable For example, if firms fear ‘panic selling’ when bad news is announced, they may delay announcement until the weekend, allowing more time for the informatlon to be digested.” French (1980) –If transaction costs are taken into account, however, trading rule based on this pattern fails to generate abnormal returns consistently. –But you may consider this effect in timing your own purchases and sales. What if?DefinitionsImplicationsPriceEmpirics

2 nd trait: Random price movements Studies of seasonality –The January effect –Keim (1983) and Roll (1983)Keim (1983) Roll (1983) –The most mystifying seasonal effect. –Stock returns, especially returns on small stocks, are on average higher in January than in other months. –Moreover, much of the higher January return on small stocks comes on the last trading day in December and the first 5 trading days in January. What if?DefinitionsImplicationsPriceEmpirics

3 rd trait: Superior trading strategy Caveat - Be careful here!!! It’s in the interest of those who find such rules to hide them rather than publicize them. Price-to-earning ratio. (P/E Ratios) Size effect What if?DefinitionsImplicationsPriceEmpirics

3 rd trait: Superior trading strategy Price-to-earning ratio. (P/E Ratios) The trading rule of “buying stocks that have low price-to-earning ratios, and avoiding stocks with high price-to-earning ratios” seems to consistently outperform the market. Question: –1) what does it mean by low P/E ratio? –2) Survivorship bias? What if?DefinitionsImplicationsPriceEmpirics

3 rd trait: Superior trading strategy Size Effect. (Banz (1981))Banz (1981) Small firms tend to have higher returns as compared to larger firms. The trading rule of “buying stocks of smaller firms” seems to consistently outperform the market. Question: –1) Is there any inherent risks of small firms not captured by risk measures? –2) Is it because transaction cost of smaller firms’ stocks are more expensive (due to thinner market)? –3) What is small? What is large? Where is the cut-off? What if?DefinitionsImplicationsPriceEmpirics

4 th trait: professional investors? If the market is semi-strong form efficient, then no matter what publicly available information mutual-fund managers rely on to pick stocks, their average returns should be the same as those of the average investor in the market as a whole. We can test efficiency by comparing the performance of professionally managed mutual funds with the performance of a market index. Evaluating mutual funds performance. (Jensen (1969))Jensen (1969) Managers of mutual funds are usually highly trained and have access to broad sources of investment information. Thus, if their managed mutual funds consistently outperform the market, then we conclude that such evidence is against the market efficiency hypothesis What if?DefinitionsImplicationsPriceEmpirics

4 th trait: professional investors? Using S & P 500 as proxy for the market, estimate the security market line. Estimate the beta for each mutual funds. Plot the mutual funds on the security market line graph (NOTE: net of all expenses!!!) What if?DefinitionsImplicationsPriceEmpirics

4 th trait: professional investors? What if?DefinitionsImplicationsPriceEmpirics

4 th trait: professional investors? What if?DefinitionsImplicationsPriceEmpirics Average Annual Return on 1493 Mutual Funds and the Market Index